ArticleNature sustainability2022
Improving biodiversity protection through artificial intelligence.
Article in Nature sustainability, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Machine-Learning Models Trained on Genomic Summary Statistics Assess Extinction Risk and Recovery Potential of Bottlenecked Populations.Evolutionary applications · 2026Article
- AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.iScience · 2026Review
- From data to decisions: The role of machine learning in precision ecological restoration.Ecological applications : a publication of the Ecological Society of America · 2026Review
- Synergies and trade-offs for reaching global biodiversity and climate commitments.Nature communications · 2026Article
- The impacts of biological invasions.Biological reviews of the Cambridge Philosophical Society · 2026Review
- The spread of non-native species.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Informing spatial conservation prioritization with species' traits.Conservation biology : the journal of the Society for Conservation Biology · 2026Article
- Climate change may increase the suitable habitats for invasive freshwater cichlids in a Neotropical basin.Scientific reports · 2025Article
- A Comprehensive Review of Remote Sensing and Artificial Intelligence Integration: Advances, Applications, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Species richness prediction and priority conservation planning for rare Michelia species in China.Scientific reports · 2025Article
- Assessing coverage of the monitoring framework of the Kunming-Montreal Global Biodiversity Framework and opportunities to fill gaps.Nature ecology & evolution · 2025Article
- Harnessing Artificial Intelligence, Machine Learning and Deep Learning for Sustainable Forestry Management and Conservation: Transformative Potential and Future Perspectives.Plants (Basel, Switzerland) · 2025Review
- Multiview angle UAV infrared image simulation with segmented model and object detection for traffic surveillance.Scientific reports · 2025Article
- MetaCity: Data-driven sustainable development of complex cities.Innovation (Cambridge (Mass.)) · 2025Review
- An African perspective to biodiversity conservation in the twenty-first century.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review.Environmental science and ecotechnology · 2024Review
- An assessment of the state of conservation planning in Europe.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Multi-disciplinary strategy to optimize irrigation efficiency in irrigated agriculture.Scientific reports · 2024Article
- Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review.Environmental science and ecotechnology · 2024Review
- Sensors for Digital Transformation in Smart Forestry.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, substantially improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resourcelimited world.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.